Traffic forecasting is an important component of an intelligent transportation system in a smart city. Research efforts have been made to manage traffic congestion using various traffic prediction models and methods. Generally, traffic prediction problems can be classified into two categories with respect to time scale: long-term and short-term. Long-term prediction provides monthly or even yearly information of traffic states, and is used for long-term transportation planning. Short-term prediction, on the other hand, provides traffic forecasts for the near future, such as 15 minutes later. It can be used by experts to guide traffic flow and to manage congestion. It may also be made available to commuters to help them plan their trips wisely. Short-term traffic prediction provides estimates of future key traffic parameters, such as speed, flow, occupancy or travel time, with a forecasting horizon typically ranging from five to thirty minutes at specific locations, given real-time and historical traffic data from relevant surveillance stations.
Bad weather conditions, such as rain, snow, fog, ice, flooding, wind and high temperature, generally result in more accidents on road. Heavy precipitation conditions may also impact traffic speed, capacity, volume, intensity, flow and travel time. Heavy rainfall may decrease the visibility and causes wet surface on roads, so road users will slow down their vehicles in order to drive safely. Although the impact of rainfall on traffic is generally recognized on an anecdotal basis, current traffic prediction systems do not provide a quantitative approach to forecasting traffic based on rainfall data.